Sonstiges: |
- Nachgewiesen in: MEDLINE
- Sprachen: English
- Publication Type: Journal Article; Review
- Language: English
- [J Neurol] 2023 Feb; Vol. 270 (2), pp. 618-631. <i>Date of Electronic Publication: </i>2022 Jul 11.
- MeSH Terms: Quality of Life* ; Frailty* ; Humans ; Aged ; Fear ; Machine Learning
- References: Allen NE, Schwarzel AK, Canning CG (2013) Recurrent falls in Parkinson’s disease: a systematic review. Parkinsons Dis 2013:1–16. https://doi.org/10.1155/2013/906274. (PMID: 10.1155/2013/906274) ; Fernando E, Fraser M, Hendriksen J et al (2017) Risk factors associated with falls in older adults with dementia: a systematic review. Physiother Canada 69:161–170. https://doi.org/10.3138/ptc.2016-14. (PMID: 10.3138/ptc.2016-14) ; Tinetti ME (2003) Clinical practice. Preventing falls in elderly persons. N Engl J Med 348:42–49. https://doi.org/10.1056/NEJMcp020719. (PMID: 10.1056/NEJMcp020719) ; Melzer I, Benjuya N, Kaplanski J (2004) Postural stability in the elderly: a comparison between fallers and non-fallers. Age Ageing 33:602–607. (PMID: 10.1093/ageing/afh218) ; Bergen G, Stevens MR, Burns ER (2016) Falls and fall injuries among adults aged ≥65 years—United States, 2014. MMWR Morb Mortal Wkly Rep 65:993–998. https://doi.org/10.15585/mmwr.mm6537a2. (PMID: 10.15585/mmwr.mm6537a2) ; Scheffer AC, Schuurmans MJ, van Dijk N et al (2008) Fear of falling: measurement strategy, prevalence, risk factors and consequences among older persons. Age Ageing 37:19–24. https://doi.org/10.1093/ageing/afm169. (PMID: 10.1093/ageing/afm169) ; Sylliaas H, Selbæk G, Bergland A (2012) Do behavioral disturbances predict falls among nursing home residents? Aging Clin Exp Res 24:251–256. https://doi.org/10.1007/BF03325253. (PMID: 10.1007/BF03325253) ; Schniepp R, Huppert A, Decker J et al (2021) Fall prediction in neurological gait disorders: differential contributions from clinical assessment, gait analysis, and daily-life mobility monitoring. J Neurol 268:3421–3434. (PMID: 10.1007/s00415-021-10504-x) ; Jahn K, Zwergal A, Schniepp R (2010) Gait disturbances in old age: classification, diagnosis, and treatment from a neurological perspective. Dtsch Arztebl Int 107:306. ; Maki BE, Zecevic A, Bateni H et al (2001) Cognitive demands of executing postural reactions: does aging impede attention switching? NeuroReport 12:3583–3587. https://doi.org/10.1097/00001756-200111160-00042. (PMID: 10.1097/00001756-200111160-00042) ; Zwergal A, Linn J, Xiong G et al (2012) Aging of human supraspinal locomotor and postural control in fMRI. Neurobiol Aging 33:1073–1084. (PMID: 10.1016/j.neurobiolaging.2010.09.022) ; Menant JC, Schoene D, Sarofim M, Lord SR (2014) Single and dual task tests of gait speed are equivalent in the prediction of falls in older people: a systematic review and meta-analysis. Ageing Res Rev 16:83–104. https://doi.org/10.1016/j.arr.2014.06.001. (PMID: 10.1016/j.arr.2014.06.001) ; Kearney FC, Harwood RH, Gladman JRF et al (2013) The relationship between executive function and falls and gait abnormalities in older adults: a systematic review. Dement Geriatr Cogn Disord 36:20–35. https://doi.org/10.1159/000350031. (PMID: 10.1159/000350031) ; Pettersson AF, Olsson E, Wahlund L-O (2005) Motor function in subjects with mild cognitive impairment and early Alzheimer’s disease. Dement Geriatr Cogn Disord 19:299–304. https://doi.org/10.1159/000084555. (PMID: 10.1159/000084555) ; Dieterich M, Brandt T (2019) Perception of verticality and vestibular disorders of balance and falls. Front Neurol 10:172. (PMID: 10.3389/fneur.2019.00172) ; Ganz DA, Bao Y, Shekelle PG, Rubenstein LZ (2007) Will my patient fall? JAMA 297:77–86. (PMID: 10.1001/jama.297.1.77) ; Studenski S, Perera S, Patel K et al (2011) Gait speed and survival in older adults. JAMA 305:50–58. (PMID: 10.1001/jama.2010.1923) ; El-Khoury F, Cassou B, Charles M-A, Dargent-Molina P (2013) The effect of fall prevention exercise programmes on fall induced injuries in community dwelling older adults: systematic review and meta-analysis of randomised controlled trials. BMJ 347. ; da Costa BR, Rutjes AWS, Mendy A et al (2012) Can falls risk prediction tools correctly identify fall-prone elderly rehabilitation inpatients? A systematic review and meta-analysis. PLoS ONE 7:e41061. https://doi.org/10.1371/journal.pone.0041061. (PMID: 10.1371/journal.pone.0041061) ; Vassallo M, Poynter L, Sharma JC et al (2008) Fall risk-assessment tools compared with clinical judgment: an evaluation in a rehabilitation ward. Age Ageing 37:277–281. https://doi.org/10.1093/ageing/afn062. (PMID: 10.1093/ageing/afn062) ; Omaña H, Bezaire K, Brady K et al (2021) Functional reach test, single-leg stance test, and tinetti performance-oriented mobility assessment for the prediction of falls in older adults: a systematic review. Phys Ther 101:pzab173. (PMID: 10.1093/ptj/pzab173) ; Lusardi MM, Fritz S, Middleton A et al (2017) Determining risk of falls in community dwelling older adults: a systematic review and meta-analysis using posttest probability. J Geriatr Phys Ther 40:1. (PMID: 10.1519/JPT.0000000000000099) ; Barry E, Galvin R, Keogh C et al (2014) Is the Timed Up and Go test a useful predictor of risk of falls in community dwelling older adults: a systematic review and meta- analysis. BMC Geriatr 14:14. https://doi.org/10.1186/1471-2318-14-14. (PMID: 10.1186/1471-2318-14-14) ; Quijoux F, Vienne-Jumeau A, Bertin-Hugault F et al (2020) Center of pressure displacement characteristics differentiate fall risk in older people: a systematic review with meta-analysis. Ageing Res Rev 62:101117. (PMID: 10.1016/j.arr.2020.101117) ; Quijoux F, Nicolaï A, Chairi I et al (2021) A review of center of pressure (COP) variables to quantify standing balance in elderly people: algorithms and open-access code. Physiol Rep 9:e15067. (PMID: 10.14814/phy2.15067) ; Cortés OL, Piñeros H, Aya PA et al (2021) Systematic review and meta-analysis of clinical trials: In–hospital use of sensors for prevention of falls. Medicine (Baltimore) 100:e27467. (PMID: 10.1097/MD.0000000000027467) ; Ferreira RN, Ribeiro NF, Santos CP (2022) Fall risk assessment using wearable sensors: a narrative review. Sensors 22:984. (PMID: 10.3390/s22030984) ; Hemmatpour M, Ferrero R, Montrucchio B, Rebaudengo M (2019) A review on fall prediction and prevention system for personal devices: evaluation and experimental results. Adv Human-computer Interact 2019:1–12. (PMID: 10.1155/2019/9610567) ; Montesinos L, Castaldo R, Pecchia L (2018) Wearable inertial sensors for fall risk assessment and prediction in older adults: a systematic review and meta-analysis. IEEE Trans Neural Syst Rehabil Eng 26:573–582. (PMID: 10.1109/TNSRE.2017.2771383) ; Sun R, Sosnoff JJ (2018) Novel sensing technology in fall risk assessment in older adults: a systematic review. BMC Geriatr 18:1–10. (PMID: 10.1186/s12877-018-0706-6) ; Balasubramanian CK (2015) The Community balance and mobility scale alleviates the ceiling effects observed in the currently used gait and balance assessments for the community-dwelling older adults. J Geriatr Phys Ther 38:78–89. https://doi.org/10.1519/JPT.0000000000000024. (PMID: 10.1519/JPT.0000000000000024) ; Mancini M, Horak FB (2010) The relevance of clinical balance assessment tools to differentiate balance deficits. Eur J Phys Rehabil Med 46:239–248. ; Ruhe A, Fejer R, Walker B (2010) The test–retest reliability of centre of pressure measures in bipedal static task conditions—a systematic review of the literature. Gait Posture 32:436–445. https://doi.org/10.1016/j.gaitpost.2010.09.012. (PMID: 10.1016/j.gaitpost.2010.09.012) ; de Sá FA, Junqueira Ferraz Baracat P (2014) Test–retest reliability for assessment of postural stability using center of pressure spatial patterns of three-dimensional statokinesigrams in young health participants. J Biomech 47:2919–2924. https://doi.org/10.1016/j.jbiomech.2014.07.010. (PMID: 10.1016/j.jbiomech.2014.07.010) ; Duarte M, Freitas S, Zatsiorsky V (2011) Control of equilibrium in humans—Sway over sway. Mot Control Oxford Univ Press, Oxford, pp 219–242. ; Ancona S, Faraci FD, Khatab E et al (2021) Wearables in the home-based assessment of abnormal movements in Parkinson’s disease: a systematic review of the literature. J Neurol. https://doi.org/10.1007/s00415-020-10350-3. (PMID: 10.1007/s00415-020-10350-3) ; Doheny EP, Walsh C, Foran T et al (2013) Falls classification using tri-axial accelerometers during the five-times-sit-to-stand test. Gait Posture 38:1021–1025. https://doi.org/10.1016/j.gaitpost.2013.05.013. (PMID: 10.1016/j.gaitpost.2013.05.013) ; Vienne A, Barrois RP, Buffat S et al (2017) Inertial sensors to assess gait quality in patients with neurological disorders: a systematic review of technical and analytical challenges. Front Psychol. https://doi.org/10.3389/fpsyg.2017.00817. (PMID: 10.3389/fpsyg.2017.00817) ; Vienne A, Moreau A, Mantilla J et al (2017) Gaze constraint while walking in progressive multiple sclerosis: a feasibility study. Neurophysiol Clin 47:354. https://doi.org/10.1016/j.neucli.2017.10.046. (PMID: 10.1016/j.neucli.2017.10.046) ; Mantilla J, Wang D, Bargiotas I et al (2020) Motor style at rest and during locomotion in humans. J Neurophysiol 123:2269–2284. https://doi.org/10.1152/jn.00019.2019. (PMID: 10.1152/jn.00019.2019) ; Bargiotas I, Moreau A, Vienne A et al (2018) Balance impairment in radiation induced leukoencephalopathy patients is coupled with altered visual attention in natural tasks. Front Neurol 9:1185. https://doi.org/10.3389/fneur.2018.01185. (PMID: 10.3389/fneur.2018.01185) ; Feise RJ (2002) Do multiple outcome measures require p-value adjustment? BMC Med Res Methodol 2:8. https://doi.org/10.1186/1471-2288-2-8. (PMID: 10.1186/1471-2288-2-8) ; Wood J, Freemantle N, King M, Nazareth I (2014) Trap of trends to statistical significance: likelihood of near significant P value becoming more significant with extra data. BMJ 348:g2215. https://doi.org/10.1136/bmj.g2215. (PMID: 10.1136/bmj.g2215) ; Bourke AK, van de Ven P, Gamble M et al (2010) Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activities. J Biomech 43:3051–3057. https://doi.org/10.1016/j.jbiomech.2010.07.005. (PMID: 10.1016/j.jbiomech.2010.07.005) ; Massie S, Forbes G, Craw S et al (2018) Fitsense: employing multi-modal sensors in smart homes to predict falls. In: International conference on case-based reasoning. Springer, pp 249–263. ; Kiprijanovska I, Gjoreski H, Gams M (2020) Detection of gait abnormalities for fall risk assessment using wrist-worn inertial sensors and deep learning. Sensors 20:5373. https://doi.org/10.3390/s20185373. (PMID: 10.3390/s20185373) ; Audiffren J, Bargiotas I, Vayatis N et al (2016) A non linear scoring approach for evaluating balance: classification of elderly as fallers and non-fallers. PLoS ONE 11:e0167456. https://doi.org/10.1371/journal.pone.0167456. (PMID: 10.1371/journal.pone.0167456) ; Bargiotas I, Kalogeratos A, Limnios M et al (2021) Revealing posturographic profile of patients with Parkinsonian syndromes through a novel hypothesis testing framework based on machine learning. PLoS ONE 16:e0246790. https://doi.org/10.1371/journal.pone.0246790. (PMID: 10.1371/journal.pone.0246790) ; Bargiotas I, Audiffren J, Vayatis N et al (2018) On the importance of local dynamics in statokinesigram: a multivariate approach for postural control evaluation in elderly. PLoS ONE 13:e0192868. https://doi.org/10.1371/journal.pone.0192868. (PMID: 10.1371/journal.pone.0192868) ; Speiser JL, Callahan KE, Houston DK et al (2021) Machine learning in aging: an example of developing prediction models for serious fall injury in older adults. J Gerontol Ser A 76:647–654. https://doi.org/10.1093/gerona/glaa138. (PMID: 10.1093/gerona/glaa138) ; Tinetti ME (1986) Performance-oriented assessment of mobility problems in elderly patients. J Am Geriatr Soc 34:119–126. https://doi.org/10.1111/j.1532-5415.1986.tb05480.x. (PMID: 10.1111/j.1532-5415.1986.tb05480.x) ; Shumway-Cook A, Brauer S, Woollacott M (2000) Predicting the probability for falls in community-dwelling older adults using the Timed Up & Go Test. Phys Ther 80:896–903. (PMID: 10.1093/ptj/80.9.896) ; Perell KL, Nelson A, Goldman RL et al (2001) Fall risk assessment measures: an analytic review. J Gerontol Ser A Biol Sci Med Sci 56:M761–M766. https://doi.org/10.1093/gerona/56.12.M761. (PMID: 10.1093/gerona/56.12.M761) ; Beauchet O, Fantino B, Allali G et al (2011) Timed up and go test and risk of falls in older adults: a systematic review. J Nutr Health Aging 15:933–938. https://doi.org/10.1007/s12603-011-0062-0. (PMID: 10.1007/s12603-011-0062-0) ; Blum L, Korner-Bitensky N (2008) Usefulness of the berg balance scale in stroke rehabilitation: a systematic review. Phys Ther 88:559–566. https://doi.org/10.2522/ptj.20070205. (PMID: 10.2522/ptj.20070205) ; Nicolai A, Audiffren J (2018) Model-space regularization and fully interpretable algorithms for postural control quantification. In: 2018 IEEE 42nd annual computer software and applications conference (COMPSAC). IEEE, pp 177–182. ; Bargiotas I, Moreau A, Vayatis N, Ricard D (2019) Predicting future falls of parkinsonians using posturography and Random Forest. In: 2019 41th annual international conference of the IEEE engineering in medicine and biology society. IEEE, Berlin. ; Bargiotas I, Audiffren J, Vayatis N et al (2019) Local assessment of statokinesigram dynamics in time: an in-depth look at the scoring algorithm. Image Process Line 9:143–157. (PMID: 10.5201/ipol.2019.251) ; Bargiotas I, Kalogeratos A, Limnios M et al (2020) Multivariate two-sample hypothesis testing through AUC maximization for biomedical applications. In: 11th hellenic conference on artificial intelligence, pp 56–59. ; Sun R, Hsieh KL, Sosnoff JJ (2019) Fall risk prediction in multiple sclerosis using postural sway measures: a machine learning approach. Sci Rep 9:16154. https://doi.org/10.1038/s41598-019-52697-2. (PMID: 10.1038/s41598-019-52697-2) ; Clémençon S, Depecker M, Vayatis N (2013) Ranking forests. J Mach Learn Res 14:39–73. ; Eichler N, Raz S, Toledano-Shubi A et al (2022) Automatic and efficient fall risk assessment based on machine learning. Sensors 22:1557. (PMID: 10.3390/s22041557) ; Liu C-L, Lee C-H, Lin P-M (2010) A fall detection system using k-nearest neighbor classifier. Expert Syst Appl 37:7174–7181. (PMID: 10.1016/j.eswa.2010.04.014) ; Breiman L (2001) Random forests. Mach Learn 45:5–32. (PMID: 10.1023/A:1010933404324) ; Chagdes J, Rietdyk S, Haddad J et al (2009) Multiple timescales in postural dynamics associated with vision and a secondary task are revealed by wavelet analysis. Exp Brain Res 197:297. (PMID: 10.1007/s00221-009-1915-1) ; Savadkoohi M, Oladunni T, Thompson LA (2021) Deep neural networks for human’s fall-risk prediction using force-plate time series signal. Expert Syst Appl 182:115220. (PMID: 10.1016/j.eswa.2021.115220) ; Nicolai A, Limnios M, Trouve A, Audiffren J (2021) A langevin-based model with moving posturographic target to quantify postural control. IEEE Trans Neural Syst Rehabil Eng 29:478–487. https://doi.org/10.1109/TNSRE.2021.3057257. (PMID: 10.1109/TNSRE.2021.3057257) ; Podsiadlo D, Richardson S (1991) The Timed “Up & Go”: a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc 39:142–148. https://doi.org/10.1111/j.1532-5415.1991.tb01616.x. (PMID: 10.1111/j.1532-5415.1991.tb01616.x) ; Pettersson B, Nordin E, Ramnemark A, Lundin-Olsson L (2020) Neither Timed Up and Go test nor Short Physical Performance Battery predict future falls among independent adults aged ≥75 years living in the community. J Frailty Sarcopenia Falls 5:24–30. https://doi.org/10.22540/JFSF-05-024. (PMID: 10.22540/JFSF-05-024) ; de Morton NA, Berlowitz DJ, Keating JL (2008) A systematic review of mobility instruments and their measurement properties for older acute medical patients. Health Qual Life Outcomes 6:44. https://doi.org/10.1186/1477-7525-6-44. (PMID: 10.1186/1477-7525-6-44) ; Vienne-Jumeau A, Quijoux F, Vidal P-P, Ricard D (2020) Wearable inertial sensors provide reliable biomarkers of disease severity in multiple sclerosis: a systematic review and meta-analysis. Ann Phys Rehabil Med 63:138–147. https://doi.org/10.1016/j.rehab.2019.07.004. (PMID: 10.1016/j.rehab.2019.07.004) ; Vienne-Jumeau A, Oudre L, Moreau A et al (2020) Personalized template-based step detection from inertial measurement units signals in multiple sclerosis. Front Neurol. https://doi.org/10.3389/fneur.2020.00261. (PMID: 10.3389/fneur.2020.00261) ; Dadashi F, Mariani B, Rochat S et al (2013) Gait and foot clearance parameters obtained using shoe-worn inertial sensors in a large-population sample of older adults. Sensors 14:443–457. https://doi.org/10.3390/s140100443. (PMID: 10.3390/s140100443) ; Dibble LE, Nicholson DE, Shultz B et al (2004) Sensory cueing effects on maximal speed gait initiation in persons with Parkinson’s disease and healthy elders. Gait Posture 19:215–225. https://doi.org/10.1016/S0966-6362(03)00065-1. (PMID: 10.1016/S0966-6362(03)00065-1) ; Glaister BC, Bernatz GC, Klute GK, Orendurff MS (2007) Video task analysis of turning during activities of daily living. Gait Posture 25:289–294. https://doi.org/10.1016/j.gaitpost.2006.04.003. (PMID: 10.1016/j.gaitpost.2006.04.003) ; Rampp A, Barth J, Schuelein S et al (2015) Inertial sensor-based stride parameter calculation from gait sequences in geriatric patients. IEEE Trans Biomed Eng 62:1089–1097. https://doi.org/10.1109/TBME.2014.2368211. (PMID: 10.1109/TBME.2014.2368211) ; Dot T, Quijoux F, Oudre L et al (2020) Non-linear template-based approach for the study of locomotion. Sensors 20:1939. https://doi.org/10.3390/s20071939. (PMID: 10.3390/s20071939) ; Mantilla J, Oudre L, Barrois R et al (2017) Template-DTW based on inertial signals: preliminary results for step characterization. In: 2017 39th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 2267–2270. ; Oudre L, Barrois-Müller R, Moreau T et al (2018) Template-based step detection with inertial measurement units. Sensors 18:4033. https://doi.org/10.3390/s18114033. (PMID: 10.3390/s18114033) ; Vienne-Jumeau A, Oudre L, Moreau A et al (2019) Comparing Gait Trials with Greedy Template Matching. Sensors 19:3089. https://doi.org/10.3390/s19143089. (PMID: 10.3390/s19143089) ; Zhou Y, Zia Ur Rehman R, Hansen C et al (2020) Classification of neurological patients to identify fallers based on spatial-temporal gait characteristics measured by a wearable device. Sensors 20:4098. https://doi.org/10.3390/s20154098. (PMID: 10.3390/s20154098) ; Kumar VC V, Ha S, Sawicki G, Liu CK (2020) Learning a control policy for fall prevention on an assistive walking device. In: 2020 IEEE international conference on robotics and automation (ICRA). IEEE, pp 4833–4840. ; Hsieh C-Y, Shi W-T, Huang H-Y et al (2018) Machine learning-based fall characteristics monitoring system for strategic plan of falls prevention. In: 2018 IEEE international conference on applied system invention (ICASI). IEEE, pp 818–821. ; Noh B, Youm C, Goh E et al (2021) XGBoost based machine learning approach to predict the risk of fall in older adults using gait outcomes. Sci Rep 11:12183. https://doi.org/10.1038/s41598-021-91797-w. (PMID: 10.1038/s41598-021-91797-w) ; Ye C, Li J, Hao S et al (2020) Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm. Int J Med Inform 137:104105. https://doi.org/10.1016/j.ijmedinf.2020.104105. (PMID: 10.1016/j.ijmedinf.2020.104105) ; Nait Aicha A, Englebienne G, Van Schooten KS et al (2018) Deep learning to predict falls in older adults based on daily-life trunk accelerometry. Sensors 18:1654. (PMID: 10.3390/s18051654) ; Tunca C, Salur G, Ersoy C (2020) Deep learning for fall risk assessment with inertial sensors: utilizing domain knowledge in spatio-temporal gait parameters. IEEE J Biomed Heal Informatics 24:1994–2005. https://doi.org/10.1109/JBHI.2019.2958879. (PMID: 10.1109/JBHI.2019.2958879) ; Barrois RP-M, Ricard D, Oudre L et al (2017) Observational study of 180° turning strategies using inertial measurement units and fall risk in poststroke hemiparetic patients. Front Neurol. https://doi.org/10.3389/fneur.2017.00194. (PMID: 10.3389/fneur.2017.00194) ; Bachlin M, Plotnik M, Roggen D et al (2010) Wearable assistant for Parkinson’s disease patients with the freezing of gait symptom. IEEE Trans Inf Technol Biomed 14:436–446. https://doi.org/10.1109/TITB.2009.2036165. (PMID: 10.1109/TITB.2009.2036165) ; Chereshnev R, Kertész-Farkas A (2018) HuGaDB: Human Gait Database for Activity Recognition from Wearable Inertial Sensor Networks. pp 131–141. ; Brajdic A, Harle R (2013) Walk detection and step counting on unconstrained smartphones. In: Proceedings of the 2013 ACM international joint conference on pervasive and ubiquitous computing. ACM, New York, NY, USA, pp 225–234. ; Truong C, Barrois-Müller R, Moreau T et al (2019) A data set for the study of human locomotion with inertial measurements units. Image Process Line 9:381–390. https://doi.org/10.5201/ipol.2019.265. (PMID: 10.5201/ipol.2019.265) ; Barrois R, Gregory T, Oudre L et al (2016) An automated recording method in clinical consultation to rate the limp in lower limb osteoarthritis. PLoS ONE 11:e0164975. https://doi.org/10.1371/journal.pone.0164975. (PMID: 10.1371/journal.pone.0164975) ; Ewenczyk C, Mesmoudi S, Gallea C et al (2017) Antisaccades in Parkinson disease: a new marker of postural control? Neurology 88:853–861. (PMID: 10.1212/WNL.0000000000003658) ; Chapman GJ, Hollands MA (2006) Evidence for a link between changes to gaze behaviour and risk of falling in older adults during adaptive locomotion. Gait Posture 24:288–294. (PMID: 10.1016/j.gaitpost.2005.10.002) ; Vitório R, Gobbi LTB, Lirani-Silva E et al (2016) Synchrony of gaze and stepping patterns in people with Parkinson’s disease. Behav Brain Res 307:159–164. (PMID: 10.1016/j.bbr.2016.04.010) ; Ajrezo L, Wiener-Vacher S, Bucci MP (2013) Saccades improve postural control: a developmental study in normal children. PLoS ONE 8:e81066. (PMID: 10.1371/journal.pone.0081066) ; Aguiar SA, Polastri PF, Godoi D et al (2015) Effects of saccadic eye movements on postural control in older adults. Psychol Neurosci 8:19. (PMID: 10.1037/h0100352) ; Leigh RJ, Zee DS (2015) The neurology of eye movements. Oxford University Press, USA. (PMID: 10.1093/med/9780199969289.001.0001) ; Ouchi Y, Okada H, Yoshikawa E et al (1999) Brain activation during maintenance of standing postures in humans. Brain 122:329–338. (PMID: 10.1093/brain/122.2.329) ; Deubel H, Schneider WX (1996) Saccade target selection and object recognition: evidence for a common attentional mechanism. Vision Res 36:1827–1837. (PMID: 10.1016/0042-6989(95)00294-4) ; Rizzolatti G, Riggio L, Dascola I, Umiltá C (1987) Reorienting attention across the horizontal and vertical meridians: evidence in favor of a premotor theory of attention. Neuropsychologia 25:31–40. (PMID: 10.1016/0028-3932(87)90041-8) ; Belenkii VE, Gurfinkel VS, Paltsev EI (1967) On the control elements of voluntary movements. Biofizika. ; Gaymard B, Lynch J, Ploner CJ et al (2003) The parieto-collicular pathway: anatomical location and contribution to saccade generation. Eur J Neurosci 17:1518–1526. (PMID: 10.1046/j.1460-9568.2003.02570.x) ; Bonnet CT, Szaffarczyk S, Baudry S (2017) Functional synergy between postural and visual behaviors when performing a difficult precise visual task in upright stance. Cogn Sci 41:1675–1693. (PMID: 10.1111/cogs.12420) ; Taghvaei S, Jahanandish MH, Kosuge K (2017) Autoregressive-moving-average hidden Markov model for vision-based fall prediction—an application for walker robot. Assist Technol 29:19–27. https://doi.org/10.1080/10400435.2016.1174178. (PMID: 10.1080/10400435.2016.1174178) ; Ting LH, McKay JL (2007) Neuromechanics of muscle synergies for posture and movement. Curr Opin Neurobiol 17:622–628. (PMID: 10.1016/j.conb.2008.01.002) ; Merfeld DM, Zupan L, Peterka RJ (1999) Humans use internal models to estimate gravity and linear acceleration. Nature 398:615. (PMID: 10.1038/19303) ; Bonan IV, Gaillard F, Ponche ST et al (2015) Early post-stroke period: a privileged time for sensory re-weighting? J Rehabil Med 47:516–522. (PMID: 10.2340/16501977-1968) ; Isableu B, Ohlmann T, Crémieux J, Amblard B (2003) Differential approach to strategies of segmental stabilisation in postural control. Exp Brain Res 150:208–221. (PMID: 10.1007/s00221-003-1446-0) ; Lacour M, Barthelemy J, Borel L et al (1997) Sensory strategies in human postural control before and after unilateral vestibular neurotomy. Exp Brain Res 115:300–310. (PMID: 10.1007/PL00005698) ; Vibert N, MacDougall HG, De Waele C et al (2001) Variability in the control of head movements in seated humans: a link with whiplash injuries? J Physiol 532:851–868. (PMID: 10.1111/j.1469-7793.2001.0851e.x) ; Sprager S, Juric MB (2015) Inertial sensor-based gait recognition: a review. Sensors 15:22089–22127. (PMID: 10.3390/s150922089) ; Kikkert LHJ, Vuillerme N, van Campen JP et al (2016) Walking ability to predict future cognitive decline in old adults: a scoping review. Ageing Res Rev 27:1–14. (PMID: 10.1016/j.arr.2016.02.001) ; Mortaza N, Abu Osman NA, Mehdikhani N (2014) Are the spatio-temporal parameters of gait capable of distinguishing a faller from a non-faller elderly. Eur J Phys Rehabil Med 50:677–691. ; Dasenbrock L, Heinks A, Schwenk M, Bauer JM (2016) Technology-based measurements for screening, monitoring and preventing frailty. Z Gerontol Geriatr 49:581–595. (PMID: 10.1007/s00391-016-1129-7) ; Schwenk M, Howe C, Saleh A et al (2014) Frailty and technology: a systematic review of gait analysis in those with frailty. Gerontology 60:79–89. (PMID: 10.1159/000354211) ; Dingwell JB, Cusumano JP (2015) Identifying stride-to-stride control strategies in human treadmill walking. PLoS ONE 10:e0124879. (PMID: 10.1371/journal.pone.0124879) ; Moore IS (2016) Is there an economical running technique? A review of modifiable biomechanical factors affecting running economy. Sport Med 46:793–807. (PMID: 10.1007/s40279-016-0474-4) ; König N, Taylor WR, Baumann CR et al (2016) Revealing the quality of movement: a meta-analysis review to quantify the thresholds to pathological variability during standing and walking. Neurosci Biobehav Rev 68:111–119. (PMID: 10.1016/j.neubiorev.2016.03.035) ; Lundberg SM, Lee S-I (2017) A unified approach to interpreting model predictions. Adv Neural Inf Process Syst 30. ; Ribeiro MT, Singh S, Guestrin C (2016) “Why should i trust you?” Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. pp 1135–1144. ; Chen C, Li O, Tao C, Barnett AJ, Rudin C, Su JK (2019) This looks like that: deep learning for interpretable image recognition. Adv Neural Informat Process Syst 32. ; Job M, Dottor A, Viceconti A, Testa M (2020) Ecological gait as a fall indicator in older adults: a systematic review. Gerontologist 60:e395–e412. (PMID: 10.1093/geront/gnz113) ; Nouredanesh M, Godfrey A, Howcroft J et al (2021) Fall risk assessment in the wild: a critical examination of wearable sensor use in free-living conditions. Gait Posture 85:178–190. (PMID: 10.1016/j.gaitpost.2020.04.010) ; Rajagopalan R, Litvan I, Jung T-P (2017) Fall prediction and prevention systems: recent trends, challenges, and future research directions. Sensors 17:2509. (PMID: 10.3390/s17112509) ; Zhao G, Chen L, Ning H (2021) Sensor-based fall risk assessment: a survey. In: Healthcare. multidisciplinary digital publishing institute, p 1448. ; Usmani S, Saboor A, Haris M et al (2021) Latest research trends in fall detection and prevention using machine learning: a systematic review. Sensors 21:5134. (PMID: 10.3390/s21155134) ; Tanwar R, Nandal N, Zamani M, Manaf AA (2022) Pathway of trends and technologies in fall detection: a systematic review. In: Healthcare. multidisciplinary digital publishing institute, p 172.
- Contributed Indexing: Keywords: Fall prediction; Force-platform; Frailty; Longitudinal follow-up; Machine learning; Wearables
- Entry Date(s): Date Created: 20220711 Date Completed: 20230201 Latest Revision: 20230313
- Update Code: 20240513
- PubMed Central ID: PMC9886639
|